Review

Experimental Studies of Evolutionary

Dynamics in Microbes

 1,2 1,2 1,2,3,

Ivana Cvijovic, Alex N. Nguyen Ba, and Michael M. Desai *

Evolutionary dynamics in laboratory microbial evolution experiments can be Highlights

surprisingly complex. In the past two decades, observations of these dynamics Laboratory evolution experiments

show that microbial populations con-

have challenged simple models of adaptation and have shown that clonal

tinue to rapidly adapt even over long

fi

interference, hitchhiking, ecological diversi cation, and contingency are timescales in simple constant environ-

ments. Beneficial mutations remain

widespread. In recent years, advances in high-throughput strain maintenance

abundant and populations typically

and phenotypic assays, the dramatically reduced cost of genome sequencing,

do not reach a fitness peak.

and emerging methods for lineage barcoding have made it possible to observe

Regular patterns of ‘global’ epistatic

evolutionary dynamics at unprecedented resolution. These new methods can

interactions as well as idiosyncratic

now begin to provide detailed measurements of key aspects of fitness

interactions between individual muta-

landscapes and of evolutionary outcomes across a range of systems. These tions lead to evolutionary contingency.

It is unclear to what extent this con-

measurements can highlight challenges to existing theoretical models and

tingency affects the predictability and

guide new theoretical work towards the complications that are most widely

repeatability of evolution.

important.

Spontaneous ecological diversification

is common, even in simple environ-

Surprising Complexity in Simple Experiments

mental conditions that were explicitly

For many decades, evolutionary adaptation in microbial populations was thought to proceed by

designed to minimize this possibility.

‘ ’ fi

periodic selection , where individual bene cial mutations arise sequentially and either go Ecological and evolutionary dynamics

extinct or fix in independent selective sweeps [1]. In this picture, evolution is relatively simple: often occur on similar timescales.

mutations arise randomly and then fix or go extinct at a rate that is commensurate with their

Measurements of the pleiotropic

individual selective effect. Our ability to predict how a population should evolve is then limited

effects of individual mutations on fit-

fi

only by our knowledge of the biological details of that speci c system (i.e., the potential ness across a range of environmental

conditions reveal diverse statistical

mutations and their corresponding mutation rates and selective effects, the biological

patterns of pleiotropy in different sys-

environment in Box 1).

tems. These patterns interact with evo-

lutionary dynamics to determine how

Beginning in the late 1990s, however, observations of surprising complexity in microbial populations specialize as they adapt.

evolution experiments provided convincing evidence rejecting this standard ‘periodic selection’

picture. Instead, careful observations of rates of fitness increase [2–4] and changes in the

frequencies of genetic markers over time [5–9] pointed to widespread signatures of clonal

interference (see Glossary) and hitchhiking. These complications make it much harder to

1

predict how evolution will act: we are limited not only by our knowledge of the biological details Department of Organismic and

Evolutionary Biology, Harvard

but also by our lack of understanding of the evolutionary dynamics themselves. The basic

University, Cambridge, MA 02138,

fi dif culty is that many interacting loci across the genome are hopelessly intertwined: evolution USA

2

cannot change the frequencies of alleles at one locus without simultaneously affecting alleles at FAS Center for Systems Biology,

Harvard University, Cambridge, MA

many other linked loci (Figure 1, Key Figure). In these settings, we cannot rely on

02138, USA

well-established models of evolution at individual loci to predict evolutionary dynamics. 3

Department of Physics, Harvard

University, Cambridge, MA 02138,

USA

Inspired in large part by these experiments, there is now a thriving theoretical community

bringing methods from statistical physics and applied mathematics to the study of evolutionary

‘ ’

dynamics in these rapidly evolving populations. This has led to many advances in our analytical *Correspondence:

understanding of the effects of clonal interference and other forms of linked selection [10]. [email protected] (M.M. Desai).

Trends in Genetics, September 2018, Vol. 34, No. 9 https://doi.org/10.1016/j.tig.2018.06.004 693

© 2018 Published by Elsevier Ltd.

Box 1. Key Determinants of Evolutionary Dynamics Glossary

Evolutionary dynamics are influenced by a number of different factors. One class of factors involves the physiology of

Clonal interference: competition

specific organisms in particular environmental contexts. We refer to these as the biological environment; they determine

between multiple different (and

how selection acts on different genotypes. Another class of factors determines how genetic variation arises and how it is

typically beneficial) mutations that are

inherited. We refer to these as the population-genetic environment; they determine how genetic drift operates, constrain

segregating simultaneously within the

how mutations move between haplotypes, and determine which organisms compete and interact. Of course, the

population.

distinction between the biological and population-genetic environment is somewhat arbitrary.

DNA barcode: a DNA sequence

that ‘barcodes’ a strain. This often

Examples of factors in the population-genetic environment include:

refers to a naturally occurring



population size, N

sequenced used for species



mutation rate, U

identification in ecological



recombination rate, R, and the physical structure of the genome

applications. In laboratory evolution, 

spatial structure.

barcodes are sometimes instead

Examples of factors in the biological environment include:

random sequences (often 10–



the ‘local’ distribution of mutational effects on fitness, r(s)

30 bp) that are integrated by the



the ruggedness of the landscape [how r(s) changes as a result of epistasis]

experimenter into a specific genomic



pleiotropic effects of mutations location.



statistics of environmental change

Epistasis: the dependence of 

ecological opportunities.

phenotypic effects of mutations on

the genetic background.

Fitness landscape: a general

mapping between genotype and

fitness in a specific environmental

However, increasingly high-resolution observations of evolutionary dynamics in laboratory

condition.

evolution experiments have continued to reveal unexpected complexities that appear to be Flow cytometry: a technique to

fl fi

crucial to evolutionary dynamics in these systems and call for still further theoretical work. Here measure the uorescence pro les of

individual cells in high throughput;

we review these recent developments.

often used to count differently

labeled cells in a population for

Studying Evolution without Phenotype applications such as fitness

Many studies of adaptation in both natural and laboratory populations are focused primarily on measurements.

Hitchhiking: the process by which a

understanding phenotypes; the goal is to characterize adaptive changes and to identify the

neutral or deleterious allele increases

evolutionary processes by which they arose as well as their genetic and molecular basis.

in frequency due to linkage to a

Experimental studies of evolutionary dynamics focus instead on understanding evolution as a beneficial mutation; can also refer to

a weakly beneficial mutation

stochastic algorithm. That is, given a particular set of biological details (i.e., the set of mutations

increasing in frequency due to

that can arise and their corresponding fitness effects in all relevant environments and genetic

linkage to a more strongly beneficial

fi

backgrounds, often referred to as the tness landscape), what will evolution actually do? one.

fi

What mutations will x with what probabilities? How repeatable is the process and what Pleiotropy: the effect of a mutation

on multiple different phenotypes.

patterns of genetic diversity will a population display? The focus is on the role of the dynamics in

Here, the phenotypes discussed are

determining evolutionary outcomes and not on the nature of the adaptive phenotypes or their

often fitness effects in different

genetic and molecular basis. That is, we aim to study evolution as a process, without reference environments.

to the specific phenotypes in question.

In principle, given a specific landscape and set of population-genetic parameters, we can

address any questions about how evolution acts by implementing computational simulations of

the dynamics. However, we cannot possibly measure the fitness landscape in every system we

wish to understand. Instead, we hope to be able to identify key principles of evolutionary

dynamics that help us understand what general features of landscapes are important and how

these features influence evolution across a wide range of systems. With this goal in mind, many

theoretical studies have focused on simple, analytically tractable models.

Since evolutionary dynamics are inherently random, testing these models involves quantifying

the probabilities of different outcomes. This requires highly controlled and replicated experi-

ments, which make it possible to identify deviations from existing theoretical predictions that

point to important new processes that future theory must account for. For example, classic

694 Trends in Genetics, September 2018, Vol. 34, No. 9

Key Figure

Simulated Example of Evolutionary Dynamics in a Microbial Population.

(A) Simulated evolutionary dynamics in an asexually evolving popula-

tion, with parameter values typical in a laboratory evolution experiment

(A) 1.0

0.8

0.6

Genotype 0.4

0.2

0.0 0 200 400 600 800 1000 GeneraƟon (B)

(i) (ii) 1.0 15% 0.8

10% 0.6

0.4 Fitness 5% 0.2 0% 0.0 (iii) 1.0 (iv) 1.0

0.8 0.8

0.6 0.6

0.4 0.4

0.2 0.2 Allele frequency Barcode frequencyBarcode frequency Marker 0.0 0.0 0 200 400 600 800 1000 0 200 400 600 800 1000

GeneraƟon GeneraƟon

(See figure legend on the bottom of the next page.)

Trends in Genetics, September 2018, Vol. 34, No. 9 695

results from population genetics tell us that the fixation probability of a beneficial mutation that is

at frequency x in a population of constant size N and provides a fitness advantage s should be

À2Nsx À2Ns

pfix(x,s) = (1 À e )/(1 À e ) [11]. This has led to the widespread view of s = 1/N as a drift

barrier: newly arising beneficial mutations with fitness effect less than this fix with probability

approximately equivalent to a neutral mutation, while beneficial mutations with larger effects fix

with probability of about 2s. Yet numerous experimental studies have shown dramatic differ-

ences from this prediction in adapting microbial populations, with beneficial mutations fixing

much less often than this formula would predict [12–17]. Analysis of the evolutionary dynamics

in these experiments showed that this discrepancy arises because beneficial mutations are

much more common than previously appreciated in these large populations (often with sizes

6 10

ranging from 10 to 10 ), leading to widespread clonal interference that reduces the efficiency

of selection and hence the fixation probability of any individual beneficial mutation. This in turn

led to further theoretical analysis of these effects of clonal interference.

We now know that clonal interference tunes the characteristic effect size of evolutionarily

relevant mutations, favoring larger-effect mutations and dramatically suppressing the impor-

tance of smaller-effect mutations [18–20]. This characteristic effect size depends sensitively on

the overall size of a population, the mutation rate, and the evolutionary conditions. Thus, these

dynamical aspects of adaptation tune the spectrum of mutations that have a chance at fixing in

populations, which in turn feeds back to affect evolutionary dynamics. On long enough time-

scales, this feedback between the raw evolutionary material and evolutionary dynamics

ultimately shapes entire genomes. In recent years there has been significant theoretical interest

in characterizing how evolutionary dynamics can alter mutation rates [21,22] and the spectrum

of available mutations in a genome [23] as well as expected patterns of epistasis [24,25].

However, often too little is known about the fitness landscape to model genomic evolution over

long evolutionary timescales meaningfully. In these cases, experimental evolution can be used

to measure the distribution of this raw evolutionary material, which can guide future theoretical

work.

As technological developments (particularly in sequencing) have recently made it possible to

observe evolutionary dynamics in laboratory populations with ever-increasing resolution and

replication, many theoretical expectations have come under challenge. are likely. Over the

coming few years, studies taking purely observational approaches are likely to continue to lead

to many insights, by simply watching evolutionary dynamics using these new tools in a variety of

Figure 1. Mutations arise often enough that they cannot be selected on individually. Instead, between the appearance of a

new mutation in a population and its eventual extinction or fixation, many other mutations arise in the population, either on

the same genetic background or in a competing lineage. As a result, the fate of each mutation is not determined only on its

own merits but is intertwined with all other mutations in the population. Most beneficial mutations are outcompeted by fitter

clones before they are able to rise to substantial frequencies. We note that these evolutionary dynamics have never been

directly observed at the resolution shown here. (B) These evolutionary dynamics can be studied in laboratory settings using

a range of methods. (i) Fitness assays. The relative increase in fitness of the evolving population compared with the

ancestor offers a coarse view of the underlying evolutionary dynamics. (ii) The frequencies of preintroduced genetic

markers through time. As with fitness assays, changes in marker frequencies reflect the aggregate effects of multiple

evolutionary events. These methods cannot resolve the effects of individual mutations. (iii) Population metagenomic

sequencing offers a view of individual mutations that arise during evolution. However, only mutations that reach substantial

frequencies (typically at least 5% or more) are observable. Thus, only a tiny and biased subset of all the mutations

occurring in the population is visible. (iv) Newer barcoding methods make it possible to observe lineage dynamics at much

higher resolution. Up to the resolution limits imposed by the evolutionary process itself (i.e., genetic drift), these lineage

dynamics can be used to infer when beneficial mutations occur and their effects on fitness. However, because barcode

diversity is lost as the population evolves, these methods are currently limited to the study of short timescales.

696 Trends in Genetics, September 2018, Vol. 34, No. 9

settings and asking whether we can explain what we see. The answer is often no, which can

then spur new theoretical directions. Similarly, these new techniques can now allow high-

throughput measurements of quantities such as distributions of pleiotropic or epistatic effects

in genomes, which were not possible to make at scale using previous methods.

Technological Advances in Observing Evolutionary Dynamics

There are three key challenges in observing evolutionary dynamics. First, the underlying events

are mutations, and we ultimately want to track the frequencies of all of the genotypes they

produce. These are typically difficult to observe directly. Second, evolutionary dynamics are

fundamentally stochastic, so we typically wish to quantify the probabilities of different events.

This often requires extensive replication. Finally, new mutations arise in single individuals. Their

dynamics, while they remain at very low frequencies in the population, are typically both critically

important and very difficult to observe.

Because it is difficult to observe the underlying mutations directly, early work attempted to

measure phenotypic changes through time. For instance, many studies measured the com-

petitive fitness of evolving lines through time (Figure 1Bi). This work provided many insights into

how quickly populations adapt [26], how this depends on population size and other parameters

[2,3,27,28], and how repeatable these phenotypic changes are across replicate populations

[29,30]. However, these phenotypic changes are a coarse view of the underlying genetic

changes and hence can provide only limited insight into the evolutionary dynamics at the

sequence level.

An alternative approach has been to engineer strains in such a way that certain specific

mutations lead to easily measurable phenotypic changes. For example, one can construct

yeast strains in which loss-of-function mutations in the gene CAN1 lead to resistance to the

drug canavanine; the frequency of these mutations can then be precisely tracked by plating on

media containing this drug [31]. Other studies built on this idea to introduce other drug or

fluorescent markers that become active when certain classes of mutations arise [13,32,33].

However, while these approaches allow us to track the frequencies of specific mutations (often

at high resolution), they typically allow us to observe only a very small fraction of the genetic

changes that occur in the population. We must infer something about the larger majority of

genetic changes that we cannot observe from the dynamics of the small fraction we can see.

Closely related to this approach, other studies have introduced neutral (or sometimes non-

neutral) genetic markers to distinguish different lineages in evolving populations [1,5,6,9,12]. By

tracking the frequencies of these markers through time, we can infer something about the

underlying dynamics [34]. However, since these studies have typically tracked the frequencies

of only two or three markers, they are sensitive only to major shifts in the composition of the

population and cannot provide any insight into dynamics at lower frequencies (Figure 1Bii).

These earlier methods for observing evolutionary dynamics were limited not only in resolution

but also in scale. The experiments themselves were typically conducted in test tubes, flasks, or

chemostats. This required substantial physical space as well as labor, which limited replication.

In addition, measuring phenotypic changes such as fitness or the frequencies of drug markers

was relatively laborious, so it was only practical to track evolution in at most a few dozen

populations at once. More recently, it has become common to maintain populations in

microplates and to maintain them using robotic liquid handling [35]. These methods make

it possible for a single experimenter to maintain thousands of microbial populations in parallel.

By using fluorescent proteins rather than drug or nutrient markers, it has similarly become

Trends in Genetics, September 2018, Vol. 34, No. 9 697

possible to analyze some aspects of the dynamics in these populations at high throughput

using flow cytometry.

More recently, advances in sequencing technology have now made it possible to track

evolution at the sequence level directly, using either whole-population ‘metagenomic’ sequenc-

ing or by sampling and sequencing individual clones [14,15,17,36–41]. While these

approaches involve substantial bioinformatics challenges, this makes it possible to identify

individual mutations and track their frequencies through time (Figure 1Biii). Although it was

initially not possible to do this at scale, reductions in sequencing costs now make it possible to

sequence hundreds of clones or whole microbial population samples to a depth of 20–100Â on

a single sequencing lane. Sample preparation, which was once a limiting factor, has also

become possible to perform at scale for minimal cost [42]. Thus, by combining extensive

sequencing with the robotic liquid handling methods described above, it is now feasible to track

evolutionary dynamics at the sequence level in hundreds of replicate populations in parallel.

However, a fundamental limit of these sequencing approaches is frequency resolution.

Sequencing errors and other bioinformatics challenges make it difficult to identify and track

mutations below a few-percentage frequency. Yet in microbial populations that often consist of

millions or billions of cells the fates of mutations are often determined by the competition of

high-fitness clones at frequencies that are many orders of magnitude lower than this. While

these challenges can be mitigated to some extent by increasing sequencing depth or by using

approaches such as circle sequencing [43] or Duplex sequencing [44] to reduce error rates, this

can dramatically increase costs. Thus, this is likely to remain a major limitation of whole-genome

sequencing approaches for the foreseeable future.

To circumvent this resolution problem, a new approach is to label individual cells with unique

DNA barcodes at the outset of an experiment [45]. This approach exploits the same principles

as older marker-tracking methods but does so using millions of unique barcodes rather than a

few fluorescent or drug markers (Figure 1Biv). By sequencing the barcode locus, one can track

the number of descendants of all individuals in the population over time at extremely high

resolution. Sequencing errors are no longer limiting because barcodes can be designed to differ

at several sites. As with other marker-based methods, this approach does not directly identify

individual mutations. However, since the barcodes measure frequencies at very high resolution,

changes in their frequencies are much more sensitive to the effects of individual mutations.

Thus, it is possible to infer when adaptive mutations occur, their effects on fitness, and their

frequency trajectories even at very low frequencies.

These barcoding methods are promising but suffer from two key limitations. First, to realize the

benefits of increased resolution, one must sequence the barcode locus at depths comparable

6 10 6 10

with microbial population sizes (10 –10 ). This requires 10 –10 reads per time point and

population sequenced and the corresponding costs limit the extent of the replication that can

be achieved. Thus far this approach has been used to track dynamics in only a few populations

in parallel [46,47]. Second, as time progresses, barcode diversity declines as some lineages go

extinct and others increase in size. Therefore, this method has been limited to offering high-

resolution views of only the earliest phases of clonal evolution. In principle, this second limitation

could be circumvented either by ‘rebarcoding’ the population at periodic intervals or by

adapting methods to continually add diversity to existing barcodes [48], although either

approach presents some technical challenges.

698 Trends in Genetics, September 2018, Vol. 34, No. 9

Which Complications Are Important?

Theoretical studies of very simple models have provided a great deal of insight that underlies

much intuition in evolutionary dynamics and population genetics. For example, many studies

have analyzed how a population climbs a single fitness peak in the strong-selection-weak-

mutation (SSWM) approximation where only one mutation is ever present in the population at a

time. Similarly, models of neutral mutation accumulation and the balance between deleterious

mutations and selection are often used to explain evolution in a ‘well-adapted’ population that is

at a local fitness peak. The implicit assumption that natural populations are typically in such a

well-adapted state underlies many practical methods in population genetics.

Of course, no one believes that evolution is ever actually this simple. Nevertheless, these

idealized models have widespread influence because there are countless complications that

could in principle matter and it is impossible to model all of them at once (Box 1). A key question

is thus: which complications are widespread and of general importance and what simplifica-

tions can we get away with? Experimental studies of evolutionary dynamics have played an

important role in answering this question. Many of these experiments are explicitly designed to

be as simple as we can make them. Thus, complications that routinely arise even in these very

artificially restricted settings may be to some extent genuinely widespread and unavoidable. Of

course this does not rule out the possibility that other effects are important in other specific

settings, but it does help to point to key factors that any theoretical picture needs to grapple

with.

For example, over the past two decades it has become clear that clonal interference and

hitchhiking are of widespread importance. Early tests of the SSWM picture focused on very

large populations, often using strains engineered to have higher than normal mutation rates, to

probe what was imagined to be an idiosyncratic regime where the widely used SSWM

approximations might begin to break down [2]. Instead, it soon became clear that clonal

interference and hitchhiking were unavoidable even in modestly sized microbial and viral

populations with wild-type mutation rates. Qualitatively similar effects of linked selection have

also been observed in recombining outbred populations adapting on standing variation, where

selection acts simultaneously on many sites across the genome, and recombination can only

slowly decouple the effects of linked beneficial and deleterious alleles (a version of the Hill–

Robertson effect) [49–53].

More recent work has also begun to challenge the assumption that populations that have

evolved in a constant environment for a long period of time can be described using the standard

picture of a well-adapted population on a fitness peak. As far as we are aware, there are no

examples that fit this picture, including the long-term experiment in Escherichia coli through at

least 60 000 generations [17,41,54,55]. Instead, even very large populations evolved for long

periods in as constant an environment as is experimentally feasible continue to increase in

fitness and to rapidly accumulate adaptive mutations. While experiments in smaller populations

do sometimes reach fitness plateaus, this is not necessarily because they have reached an

optimum [56]. Instead, these populations may have reached a balance between adaptation and

the stochastic accumulation of deleterious mutations [57], with molecular evolution continuing

at a rapid pace. These results suggest that we should question the standard picture of natural

populations in a well-adapted state characterized by neutral evolution and deleterious

mutation–selection balance.

Another widespread assumption of many models of evolution and population genetics is that

evolutionary and ecological dynamics can be separated. Instead, coexisting types often

Trends in Genetics, September 2018, Vol. 34, No. 9 699

spontaneously arise in laboratory evolution experiments and are maintained for long periods Outstanding Questions

fi

due to negative frequency-dependent selection [16,17,37,38,58–61]. These ecological inter- How rugged are tness landscapes?

What do the statistical patterns of epis-

actions arise via a variety of different mechanisms, despite the fact that many of these

tasis between mutations look like?

experiments were explicitly designed to minimize the opportunities for ecological diversification.

How do these in turn impact evolution-

fi

These ecological interactions are then often further modi ed as evolution continues in each ary dynamics?

coexisting type, leading to shifts in the frequencies of the types [17,37,38,59,60]. Thus,

fi fi

evolution and ecology are fundamentally intertwined. How common are modi ers of tness

landscapes (e.g., mutator alleles,

evolvability loci) and what are their

Other types of complex frequency-dependent interactions are also sometimes observed. For

effects on evolutionary dynamics?

example, there are some reports of positive frequency-dependent and nontransitive (or ‘Red

’ fi –

Queen ) tness interactions [62 65]. However, within the limits of current resolution, these more How does recombination affect these

patterns? What do genome-wide pat-

complex effects appear to be relatively rare in microbial evolution experiments [66].

terns of epistatic effects look like in

recombining populations? Are closely

The effects of individual mutations can strongly depend on the genetic background in which

linked sites more or less likely to have

fi they occur [67]. These epistatic effects of course include speci c interactions involving muta- epistatic interactions than distant

sites?

tions in an individual protein or pathway [68–70]. However, some experiments have shown

more widespread effects, where individual mutations can often alter the future evolutionary

How often do ecological interactions

potential of their descendants. This can occur both due to global fitness-mediated effects

between closely related individuals

[30,71–77] (e.g., higher-fitness genotypes can be generically less ‘adaptable’ and less ‘robust’)

arise? How often do they lead to

and due to more idiosyncratic mechanisms [78–80]. This widespread epistasis and contin- robust equilibria and how often are

these ecological interactions broken

gency may help to explain why experimentally evolving populations do not ever appear to reach

by future mutations?

a fitness peak.

How different is adaptation in different

fi

Similarly, the structure of pleiotropic effects of mutations for tness across varying environ- environments? In what ways is labora-

mental conditions can be complex. While we might expect simple tradeoffs between fitness tory adaptation similar to adaptation in

more ‘natural’ settings and in what

under different conditions to routinely arise from physiological constraints, the reality is often

ways do they differ?

more subtle [81–87]. There are often multiple distinct ways that a population can adapt to a

given environmental condition, which may have a variety of effects across other environments

How does variability in the evolutionary

– fl

[88 90]. The details of the population genetic environment and the statistics of uctuating environment impact evolutionary

conditions can therefore play a critical role in determining the extent to which adaptation tends dynamics and the predictability of evo-

lution? What are the pleiotropic effects

to lead to specialization [91].

of mutations across a range of environ-

ments and how does this affect evo-

Concluding Remarks and Future Perspectives lution under conditions that fluctuate in

time or space?

It could be argued that studies of evolutionary dynamics in artificial and highly simplified laboratory

conditions (which often lack spatial structure, temporal variability, interactions with other species,

and other complexities) are unrepresentative of evolution in natural systems. However, we view

this simplicity instead as a major strength of experimental evolution, which is a powerful tool

precisely because complications can be introduced in a controlled, replicable way (see Outstand-

ing Questions). Nevertheless, one could argue that conclusions from artificial laboratory environ-

ments are simply not representative of those relevant in more ‘natural’ settings. Testing this will

ultimately require more detailed direct observations of evolution in natural environments. Some

recent work has moved in this direction by using laboratory evolution techniques in more complex

and realistic environments, such as by studying E. coli that are experimentally passaged through

mouse guts [39] or Vibrio fischeri living in symbiosis with squid [92]. A complementary direction will

be to begin to use these general techniques and analysis frameworks to study evolution directly in

natural systems, such as evolving pathogens [93–95], the immune system [96], and host-

associated microbial and viral communities [97–99].

It is also unclear how evolutionary dynamics in microbial populations relate to other systems.

Numerous studies have analyzed evolution in other laboratory model organisms, including

700 Trends in Genetics, September 2018, Vol. 34, No. 9

complex multicellular organisms such as Drosophila [51,53] and Caenorhabditis elegans [100].

In these systems, standing genetic variation, differences in genome organization and ploidy,

and other complications can all influence the dynamics. These factors may affect which

parameter regimes are typically relevant and what complications theoretical models must

grapple with. However, many of the technical methods described here cannot be directly

applied to these systems. Thus an important future direction will be to develop tools that make it

possible to study evolution in these more complex organisms at higher throughput and

resolution and compare the results to what we have learned by studying microbial systems.

Acknowledgments

M.M.D. acknowledges support from the Simons Foundation (grant 376196), the National Science Foundation (DEB-

1655960), and the National Institutes of Health (R01-GM104239).

References

1. Atwood, K.C. et al. (1951) Periodic selection in Escherichia coli. 19. Hallatschek, O. (2011) The noisy edge of traveling waves. Proc.

Proc. Natl. Acad. Sci. U. S. A. 37, 146–155 Natl. Acad. Sci. U. S. A. 108, 1783–1787

2. de Visser, J. et al. (1999) Diminishing returns from mutation 20. Fisher, D.S. (2013) Asexual evolution waves: fluctuations and

supply rate in asexual populations. Science 283, 404–406 universality. J. Stat. Mech. Theory Exp. 2013, P01011

3. Desai, M.M. et al. (2007) The speed of evolution and mainte- 21. Sung, W. et al. (2012) Drift-barrier hypothesis and mutation-rate

nance of variation in asexual populations. Curr. Biol. 17, evolution. Proc. Natl. Acad. Sci. 109, 18488–18492

385–394

22. Good, B.H. and Desai, M.M. (2016) Evolution of mutation rates

4. Miralles, R. et al. (2000) Diminishing returns of population size in in rapidly adapting asexual populations. Genetics 204,

the rate of RNA virus adaptation. J. Virol. 74, 3566–3571 1249–1266

5. Kao, K.C. and Sherlock, G. (2008) Molecular characterization of 23. Rice, D.P. et al. (2015) The evolutionarily stable distribution of

clonal interference during adaptive evolution in asexual popu- fitness effects. Genetics 200, 321–329

lations of Saccharomyces cerevisiae. Nat. Genet. 40,

24. Neher, R.A. and Shraiman, B.I. (2011) Statistical genetics and

1499–1504

evolution of quantitative traits. Rev. Mod. Phys. 83,

6. Hegreness, M. et al. (2006) An equivalence principle for the 1283–1300

incorporation of favorable mutations in asexual populations.

25. de Visser, J.A.G.M. et al. (2011) The causes of epistasis. Proc.

Science 311, 1615–1617

R. Soc. B Biol. Sci. 278, 3617–3624

7. Imhof, M. and Schlotterer, C. (2001) Fitness effects of advanta-

26. Lenski, R. et al. (1991) Long-term experimental evolution in

geous mutations in evolving Escherichia coli populations. Proc.

Escherichia coli I. Adaptation and divergence during 2,000

Natl. Acad. Sci. U. S. A. 98, 1113–1117

generations. Am. Nat. 138, 1315–1341

8. Miralles, R. et al. (1999) Clonal interference and the evolution of

27. Kryazhimskiy, S.K. et al. (2012) Population subdivision and

RNA viruses. Science 285, 1745–1747

adaptation in asexual populations of Saccharomyces cerevisiae.

9. De Visser, J.A.G.M. and Rozen, D.E. (2006) Clonal interference Evolution 66, 1931–1941

and the periodic selection of new beneficial mutations in Escher-

28. Nahum, J.R. et al. (2015) A tortoise–hare pattern seen in adapt-

ichia coli. Genetics 172, 2093–2100

ing structured and unstructured populations suggests a rugged

10. Neher, R.A. (2013) Genetic draft, selective interference, and fitness landscape in bacteria. Proc. Natl. Acad. Sci. 112,

population genetics of rapid adaptation. Annu. Rev. Ecol. Evol. 7530–7535

Syst. 44, 195–215

29. Travisano, M. et al. (1995) Experimental tests of the roles of

11. Kimura, M. (1962) On the probability of fixation of mutant genes adaptation, chance, and history in evolution. Science 267,

in a population. Genetics 47, 713–719 87–90

12. Frenkel, E.M. et al. (2014) The fates of mutant lineages and the 30. Kryazhimskiy, S. et al. (2014) Global epistasis makes adaptation

distribution of fitness effects of beneficial mutations in laboratory predictable despite sequence-level stochasticity. Science 344,

budding yeast populations. Genetics 196, 1217–1226 1519–1522

13. Lang, G.I. et al. (2011) Genetic variation and the fate of beneficial 31. Paquin, C. and Adams, J. (1983) Frequency of fixation of adap-

mutations in asexual populations. Genetics 188, 647–661 tive mutations is higher in evolving diploid than haploid yeast

populations. Nature 302, 495–500

14. Lang, G.I. et al. (2013) Pervasive genetic hitchhiking and clonal

interference in forty evolving yeast populations. Nature 500, 32. MacLean, R.C. et al. (2010) Diminishing returns from beneficial

571–574 mutations and pervasive epistasis shape the fitness landscape

for rifampicin resistance in Pseudomonas aeruginosa. Genetics

15. Kvitek, D.J. and Sherlock, G. (2013) Whole genome, whole

186, 1345–1354

population sequencing reveals that loss of signaling networks

is the major adaptive strategy in a constant environment. PLoS 33. Baym, M. et al. (2016) Spatiotemporal microbial evolution on

Genet. 9, e1003972 antibiotic landscapes. Science 353, 1147–1151

16. Madamsetti, R. et al. (2015) Adaptation, clonal interference, and 34. Zhang, W. et al. (2012) Estimation of the rate and effect of new

frequency-dependent interactions in a long-term evolution beneficial mutations in asexual populations. Theor. Popul. Biol.

experiment with Escherichia coli. Genetics 200, 619–631 81, 168–178

17. Good, B.H. et al. (2017) The dynamics of molecular evolution 35. Desai, M.M. (2013) Statistical questions in experimental

over 60,000 generations. Nature 551, 45–50 evolution. J. Stat. Mech. Theory Exp. 2013, P01003

18. Good, B.H. et al. (2012) Distribution of fixed beneficial mutations 36. McDonald, M.J. et al. (2016) Sex speeds adaptation by

and the rate of adaptation in asexual populations. Proc. Natl. altering the dynamics of molecular evolution. Nature 531,

Acad. Sci. U. S. A. 109, 4950–4955 233–236

Trends in Genetics, September 2018, Vol. 34, No. 9 701

37. Traverse, C.C. et al. (2013) Tangled bank of experimentally 62. Rendueles, O. et al. (2015) Positively frequency-dependent

evolved Burkholderia biofilms reflects selection during chronic interference competition maintains diversity and pervades a

infections. Proc. Natl. Acad. Sci. 110, E250–E259 natural population of cooperative microbes. Curr. Biol. 25,

1673–1681

38. Herron, M.D. and Doebeli, M. (2013) Parallel evolutionary

dynamics of adaptive diversification in Escherichia coli. PLoS 63. Chao, L. and Levin, B.R. (1981) Structured habitats and the

Biol. 11, e1001490 evolution of anticompetitor toxins in bacteria. Proc. Natl. Acad.

Sci. 78, 6324–6328

39. Barroso-Batista, J. et al. (2014) The first steps of adaptation of

Escherichia coli to the gut are dominated by soft sweeps. PLoS 64. Paquin, C.E. and Adams, J. (1983) Relative fitness can decrease

Genet. 10, e1004182 in evolving asexual populations of S. cerevisiae. Nature 306, 368

40. Barrick, J.E. et al. (2009) Genome evolution and adaptation in a 65. Kerr, B. et al. (2002) Local dispersal promotes biodiversity in a

long-term experiment with Escherichia coli. Nature 461, real-life game of rock–paper–scissors. Nature 418, 171–174

1243–1247

66. de Visser, J.A. and Lenski, R.E. (2002) Long-term experimental

41. Tenaillon, O. et al. (2016) Tempo and mode of genome evolution evolution in Escherichia coli. XI. Rejection of non-transitive inter-

in a 50,000-generation experiment. Nature 536, 165–170 actions as cause of declining rate of adaptation. BMC Evol. Biol.

2, 19

42. Baym, M. et al. (2015) Inexpensive multiplexed library prepara-

tion for megabase-sized genomes. PLoS One 10, e0128036 67. Jerison, E.R. and Desai, M.M. (2015) Genomic investigations of

evolutionary dynamics and epistasis in microbial evolution

43. Lou, D.I. et al. (2013) High-throughput DNA sequencing errors

experiments. Curr. Opin. Genet. Dev. 35, 33–39

are reduced by orders of magnitude using circle sequencing.

Proc. Natl. Acad. Sci. 110, 19872–19877 68. Weinreich, D.M. et al. (2006) Darwinian evolution can follow only

very few mutational paths to fitter proteins. Science 312,

44. Schmitt, M.W. et al. (2012) Detection of ultra-rare mutations by

111–114

next-generation sequencing. Proc. Natl. Acad. Sci. 109,

14508–14513 69. de Vos, M.G.J. et al. (2013) Environmental dependence of

genetic constraint. PLoS Genet. 9, e1003580

45. Blundell, J.R. and Levy, S.F. (2014) Beyond genome sequenc-

ing: Lineage tracking with barcodes to study the dynamics of 70. Tenaillon, O. et al. (2012) The molecular diversity of adaptive

evolution, infection, and cancer. Genomics 104, 417–430 convergence. Science 335, 457–461

46. Levy, S.F. et al. (2015) Quantitative evolutionary dynamics using 71. Jerison, E.R. et al. (2017) Genetic variation in adaptability and

high-resolution lineage tracking. Nature 519, 181–186 pleiotropy in budding yeast. eLife 6, e27167

47. Blundell, J.R. et al. (2017) The dynamics of adaptive genetic 72. Moore, F. et al. (2006) Pervasive compensatory adaptation in

diversity during the early stages of clonal evolution. bioRxiv Escherichia coli. Proc. R. Soc. Lond. B 2006

170589

73. Perfeito, L. et al. (2014) Rates of fitness decline and rebound

48. Kalhor, R. et al. (2016) Rapidly evolving homing CRISPR suggest pervasive epistasis. Evolution 68, 150–162

barcodes. Nat. Methods 14, 195

74. Barrick, J.E. et al. (2010) Escherichia coli rpoB mutants have

49. Kosheleva, K. and Desai, M.M. (2018) Recombination alters the increased evolvability in proportion to their fitness defects. Mol.

dynamics of adaptation on standing variation in laboratory yeast Biol. Evol. 27, 1338–1347

populations. Mol. Biol. Evol. 35, 180–201

75. Sanjuan, R. et al. (2005) Epistasis and the adaptability of an RNA

50. Parts, L. et al. (2011) Revealing the genetic structure of a trait by virus. Genetics 170, 1001–1008

sequencing a population under selection. Genome Res. 21,

76. Khan, A.I. et al. (2011) Negative epistasis between beneficial

1131–1138

mutations in an evolving bacterial population. Science 332,

51. Burke, M.K. et al. (2010) Genome-wide analysis of a long-term 1193–1196

evolution experiment with Drosophila. Nature 467, 587

77. Chou, H.-H. et al. (2011) Diminishing returns epistasis among

52. Burke, M.K. et al. (2014) Standing genetic variation drives beneficial mutations decelerates adaptation. Science 332,

repeatable experimental evolution in outcrossing populations 1190–1192

of Saccharomyces cerevisiae. Mol. Biol. Evol. 31,

78. Woods, R.J. et al. (2011) Second-order selection for evolvability

3228–3239

in a large Escherichia coli population. Science 331, 1433–1436

53. Orozco-terWengel, P. et al. (2012) Adaptation of Drosophila to a

79. Blount, Z.D. et al. (2008) Historical contingency and the evolu-

novel laboratory environment reveals temporally heterogeneous

tion of a key innovation in an experimental population of Escher-

trajectories of selected alleles. Mol. Ecol. 21, 4931–4941

ichia coli. PNAS 105, 7899–7906

54. Wiser, M.J. et al. (2013) Long-term dynamics of adaptation in

80. Blount, Z.D. et al. (2012) Genomic analysis of a key innovation in

asexual populations. Science 342, 1364

an experimental Escherichia coli population. Nature 489,

55. Lenski, R.E. et al. (2015) Sustained fitness gains and variability in 513–518

fitness trajectories in the long-term evolution experiment with

81. Cooper, V.S. and Lenski, R.E. (2000) The population genetics of

Escherichia coli. Proc. R. Soc. B Biol. Sci. 282, 20152292

ecological specialization in evolving Escherichia coli populations.

56. Silander, O.K. et al. (2007) Understanding the evolutionary fate Nature 407, 736–739

of finite populations: the dynamics of mutational effects. PLoS

82. Leiby, N. and Marx, C.J. (2014) Metabolic erosion primarily

Biol. 5, e94

through mutation accumulation, and not tradeoffs, drives limited

57. Goyal, S. et al. (2012) Dynamic mutation–selection balance as evolution of substrate specificity in Escherichia coli. PLoS Biol.

an evolutionary attractor. Genetics 191, 1309–1319 12, e1001789

58. Rainey, P.B. and Travisano, M. (1998) Adaptive radiation in a 83. MacLean, R.C. et al. (2004) The evolution of a pleiotropic fitness

heterogeneous environment. Nature 394, 69–72 tradeoff in Pseudomonas fluorescens. Proc. Natl. Acad. Sci.

U. S. A. 101, 8072–8077

59. Frenkel, E.M. et al. (2015) Crowded growth leads to the spon-

taneous evolution of semistable coexistence in laboratory yeast 84. Duffy, S. et al. (2006) Pleiotropic costs of niche expansion in the

populations. Proc. Natl. Acad. Sci. 112, 11306–11311 RNA bacteriophage Phi6. Genetics 172, 751–757

60. Plucain, J. et al. (2014) Epistasis and allele specificity in the 85. Jasmin, J.-N. and Zeyl, C. (2013) Evolution of pleiotropic costs in

emergence of a stable polymorphism in Escherichia coli. Sci- experimental populations. J. Evol. Biol. 26, 1363–1369

ence 343, 1366

86. Li, Y. et al. (2018) Hidden complexity of yeast adaptation under

61. Behringer, M.G. et al. (2018) Escherichia coli cultures maintain simple evolutionary conditions. Curr. Biol. 28, 515–525.e6

stable subpopulation structure during long-term evolution. Proc.

87. Meyer, J.R. et al. (2015) Biophysical mechanisms that maintain

Natl. Acad. Sci. 115, E4642–E4650

biodiversity through trade-offs. Nat. Commun. 6, 6278

702 Trends in Genetics, September 2018, Vol. 34, No. 9

88. Rodríguez-Verdugo, A. et al. (2014) Different tradeoffs result 95. Lieberman, T.D. et al. (2011) Parallel bacterial evolution within

from alternate genetic adaptations to a common environment. multiple patients identifies candidate pathogenicity genes. Nat.

Proc. Natl. Acad. Sci. 111, 12121–12126 Genet. 43, 1275–1280

89. Bennett, A.F. and Lenski, R.E. (2007) An experimental test of 96. Horns, F. et al. (2017) Signatures of selection in the human

evolutionary trade-offs during temperature adaptation. Proc. antibody repertoire: selective sweeps, competing subclones,

Natl. Acad. Sci. 104, 8649–8654 and neutral drift. bioRxiv 145052

90. Ostrowski, E.A. et al. (2005) Pleiotropic effects of beneficial 97. Zhao, S. et al. (2018) Adaptive evolution within the gut

mutations in Escherichia coli. Evolution 59, 2343–2352 microbiome of individual people. bioRxiv 208009

91. Turner, P. and Elena, S. (2000) Cost of host radiation in an RNA 98. Garud, N.R. et al. (2018) Evolutionary dynamics of bacteria in the

virus. Genetics 156, 1465–1470 gut microbiome within and across hosts. bioRxiv 210955

92. Soto, W. et al. (2012) Evolutionary perspectives in a mutualism 99. Minot, S. et al. (2013) Rapid evolution of the human gut virome.

of sepiolid squid and bioluminescent bacteria: combined usage Proc. Natl. Acad. Sci. U. S. A. 110, 12450–12455

of microbial experimental evolution and temporal population

100. Teotónio, H. et al. (2017) Experimental evolution with

genetics. Evolution 66, 1308–1321

Caenorhabditis nematodes. Genetics 206, 691–716

93. Luksza, M. and Lassig, M. (2014) A predictive fitness model for

influenza. Nature 507, 57–61

94. Zanini, F. et al. (2015) Population genomics of intrapatient HIV-1

evolution. eLife 4, e11282

Trends in Genetics, September 2018, Vol. 34, No. 9 703